臺灣博碩士論文加值系統

多輸入多輸出（MIMO）傳輸已經是一種用來增加頻寬效益的眾所周知的技術。同時，如何設計出在多輸入多輸出通道使用的低運算量的接收器仍然是一項艱難的挑戰。最大概似偵測器（ML detector）可以達到極好的效能，然而其所需的計算量是非常龐大的。使用球體解碼演算法（Sphere Decoding Algorithm）可以達到和最大概似偵測器一樣的效能，並且可以降低大量的運算量。在此篇論文中，我們提出了一種較實用的球體解碼演算法。利用了一種簡單而且有效率的方法來設定球體的半徑初值，其扮演著決定計算量的一個很重要的角色。此外，我們利用一種增加虛擬天線的架構使得球體演算法能被應用在當傳送天線個數大於接收天線個數的情況；這個方法增加了球體解碼演算法更大的可用性，而且不致改變它原有的低運算量及高效能的特性。

Multi-Input Multi-Output (MIMO) transmission has become a popular technique to increase spectral efficiency. Meanwhile, the design of cost-effective receivers for MIMO channels remains a challenging task. Maximum-Likelihood (ML) detector can achieve superb performance, yet the computational complexity is enormously high. Receivers based on sphere decoding (SD) reach the performance of ML detectors, and potentially a great deal of computational cost can be saved. In this thesis, a practical sphere-decoding algorithm is proposed. It utilizes a simple and effective way to set the initial radius which plays a decisive role in determining the computational complexity. Furthermore, a pseudo-antenna augmentation scheme is employed such that sphere decoding can be applied where the number of receive antennas is less than that of transmit antennal; thus enhance the applicability of this powerful algorithm.

中文摘要……………………………………………………………I
ABSTRACT………………………………………………………...II
誌謝………………………………………………………………III
ACKNOWLEDGE
CONTENTS......……………………………..................IV
LIST OF TABLES……………………………………………………VI
LIST OF FIGURES………………………………………………….VI
SYMBOLS……………………………………………………………VII

Chapter 1.............................................1
Introduction..........................................1
1.1 Introduction......................................1
1.2 Thesis Outline....................................1

Chapter 2.............................................2
Multiple-Input Multiple-Output........................2
2.1 MIMO System Model.................................2
2.2 MIMO Receivers....................................3
2.2.1 Linear Detection Methods........................3
2.2.2 Nulling and Cancelling..........................3
2.2.3 Brute-Force Maximum Likelihood (ML) Detection...4
2.2.4 Sphere Decoding (SD)............................5

Chapter 3.............................................8
Sphere Decoding Algorithm.............................8

Chapter 4............................................11
The Proposed Sphere Decoding Algorithm...............11
4.1 Setting the Radius...............................11
4.2 A Pseudo-Antenna Augmentation Scheme.............13

Chapter 5............................................20
Simulation Results...................................20

Chapter 6............................................23
Conclusion...........................................23

Future work..........................................24
Appendix.............................................29
Reference............................................31

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